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Mastering Matplotlib

Mastering Matplotlib

By : Duncan M. McGreggor, Duncan M McGreggor
3.5 (8)
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Mastering Matplotlib

Mastering Matplotlib

3.5 (8)
By: Duncan M. McGreggor, Duncan M McGreggor

Overview of this book

If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.
Table of Contents (11 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Up to Speed
1. Getting Up to Speed
A brief historical overview of matplotlib
What's new in matplotlib 1.4
The intermediate matplotlib user
Prerequisites for this book
Python 3
Coding style
Installing matplotlib
Using IPython Notebooks with matplotlib
Advanced plots – a preview
Setting up the interactive backend
Summary
2
2. The matplotlib Architecture
2. The matplotlib Architecture
The original design goals
The current matplotlib architecture
The backend layer
The artist layer
The scripting layer
The supporting components of the matplotlib stack
matplotlib modules
The execution flow
The matplotlib architecture as it relates to this book
Summary
3
3. matplotlib APIs and Integrations
3. matplotlib APIs and Integrations
The procedural pylab API
The pyplot scripting API
The matplotlib object-oriented API
matplotlib in other frameworks
Summary
4
4. Event Handling and Interactive Plots
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4. Event Handling and Interactive Plots
Event loops in matplotlib
Event handling
Summary
5
5. High-level Plotting and Data Analysis
5. High-level Plotting and Data Analysis
High-level plotting
Data analysis
Summary
6
6. Customization and Configuration
6. Customization and Configuration
Customization
Configuration
Summary
7
7. Deploying matplotlib in Cloud Environments
7. Deploying matplotlib in Cloud Environments
Making a use case for matplotlib in the Cloud
An example – AWS and Docker
Summary
8
8. matplotlib and Big Data
8. matplotlib and Big Data
Big data
Working with large data sources
Visualizing large data
Other visualization tools
Summary
9
9. Clustering for matplotlib
9. Clustering for matplotlib
Clustering and parallel programming
The custom ZeroMQ cluster
Clustering with IPython
More clustering
Summary
10
Index
Index

Event handling

We now have a good background for some practical work with matplotlib events. We are now ready to explore the nitty-gritty. Let's begin with the list of events that matplotlib supports:

Event name

Class

Description

button_press_event

MouseEvent

The mouse button is pressed

button_release_event

MouseEvent

The mouse button is released

draw_event

DrawEvent

The canvas draw occurs

key_press_event

KeyEvent

A key is pressed

key_release_event

KeyEvent

A key is released

motion_notify_event

MouseEvent

Motion of the mouse

pick_event

PickEvent

An object in the canvas is selected

resize_event

ResizeEvent

The figure canvas is resized

scroll_event

MouseEvent

The scroll wheel of the mouse is rolled

figure_enter_event

LocationEvent

The mouse enters a figure

figure_leave_event

LocationEvent

The mouse leaves a figure

axes_enter_event

LocationEvent

The mouse enters an axes object

axes_leave_event

LocationEvent

The mouse leaves an axes object

Note...

End of Section 3
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